U.S. patent application number 16/998380 was filed with the patent office on 2021-03-04 for tire wear state estimation system and method employing footprint shape factor.
The applicant listed for this patent is The Goodyear Tire & Rubber Company. Invention is credited to Mustafa Ali Arat, Kanwar Bharat Singh.
Application Number | 20210061020 16/998380 |
Document ID | / |
Family ID | 1000005048879 |
Filed Date | 2021-03-04 |
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United States Patent
Application |
20210061020 |
Kind Code |
A1 |
Singh; Kanwar Bharat ; et
al. |
March 4, 2021 |
TIRE WEAR STATE ESTIMATION SYSTEM AND METHOD EMPLOYING FOOTPRINT
SHAPE FACTOR
Abstract
A tire wear state estimation system includes a first sensor unit
that is mounted on a tire and includes a footprint centerline
length measurement sensor. A second sensor unit is mounted on the
tire and includes a shoulder length measurement sensor. At least
one of the first sensor unit and the second sensor unit includes a
pressure sensor, a temperature sensor, and electronic memory
capacity for storing tire identification information. A processor
is in electronic communication with the sensor units and receives
the measured centerline length, the measured pressure, the measured
temperature, the identification information, and the measured
shoulder length. The identification information is correlated to
tire construction data. An analysis module stored on the processor
receives the measured values, the identification information, and
the tire construction data as inputs. The analysis module includes
a prediction model that generates an estimated wear state for the
tire from the inputs.
Inventors: |
Singh; Kanwar Bharat;
(Lorenztweiler, LU) ; Arat; Mustafa Ali;
(Ettelbruck, LU) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
The Goodyear Tire & Rubber Company |
Akron |
OH |
US |
|
|
Family ID: |
1000005048879 |
Appl. No.: |
16/998380 |
Filed: |
August 20, 2020 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
|
|
62893860 |
Aug 30, 2019 |
|
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Current U.S.
Class: |
1/1 |
Current CPC
Class: |
B60C 2019/004 20130101;
B60C 19/00 20130101; B60C 11/246 20130101; G01M 17/02 20130101;
B60C 11/243 20130101; H04L 2012/40273 20130101; H04L 12/40
20130101 |
International
Class: |
B60C 11/24 20060101
B60C011/24; G01M 17/02 20060101 G01M017/02; B60C 19/00 20060101
B60C019/00; H04L 12/40 20060101 H04L012/40 |
Claims
1. A tire wear state estimation system comprising: a vehicle; a
tire supporting the vehicle; a first sensor unit being mounted on
the tire, the first sensor unit including a footprint centerline
length measurement sensor to measure a centerline length of a
footprint of the tire; a second sensor unit being mounted on the
tire, the second sensor unit including a shoulder length
measurement sensor to measure a shoulder length of the footprint of
the tire; wherein at least one of the first sensor unit and the
second sensor unit includes: a pressure sensor to measure a
pressure of the tire; a temperature sensor to measure a temperature
of the tire; and electronic memory capacity for storing
identification information for the tire; a processor in electronic
communication with the first sensor unit and the second sensor
unit, the processor receiving the measured centerline length, the
measured pressure, the measured temperature, the identification
information, and the measured shoulder length; a tire construction
database storing tire construction data, the tire construction
database being in electronic communication with the processor,
wherein the identification information is correlated to the tire
construction data; an analysis module being stored on the processor
and receiving the measured centerline length, the measured
pressure, the measured temperature, the identification information,
the tire construction data, and the measured shoulder length as
inputs; and the analysis module including a prediction model to
generate an estimated wear state for the tire from the inputs.
2. The tire wear state estimation system of claim 1, wherein the
first sensor unit is attached to an innerliner of the tire at an
equatorial centerplane of the tire, and the second sensor is
attached to the innerliner and is spaced laterally apart from the
first sensor unit.
3. The tire wear state estimation system of claim 1, wherein the
analysis module further comprises a denormalization filter
receiving the measured centerline length, the measured pressure,
the measured temperature, the identification information, and the
tire construction data as inputs, and generating a normalized
footprint length.
4. The tire wear state estimation system of claim 3, wherein the
analysis module further comprises a historical footprint
measurement database storing a historical log of footprint
measurements, the historical footprint measurement database being
in electronic communication with the processor, and wherein the
normalized footprint length is correlated to the historical log of
footprint measurements, and an average of the values is taken.
5. The tire wear state estimation system of claim 4, wherein the
analysis module further comprises a time filter, wherein the
average of the values is applied to the time filter to account for
time-scale decomposition of the tire.
6. The tire wear state estimation system of claim 5, wherein the
analysis module further comprises a footprint shape factor
calculator, which calculates a footprint shape factor from the
measured centerline length and the measured shoulder length, and
yields a regularized footprint length for the tire.
7. The tire wear state estimation system of claim 6, wherein the
regularized footprint length is input into the prediction
model.
8. The tire wear state estimation system of claim 1, further
comprising a vehicle-mounted collection unit to collect
measurements of a speed of the vehicle and an inertia of the
vehicle; wherein the analysis module receives the speed of the
vehicle and the inertia of the vehicle as inputs; and the analysis
module includes an event filter, wherein the speed of the vehicle
and the inertia of the vehicle are compared to threshold values
before further analysis is performed by the analysis module.
9. The tire wear state estimation system of claim 8, wherein the
speed of the vehicle is calculated from global positioning system
data and the inertia of the vehicle is measured with an
accelerometer.
10. The tire wear state estimation system of claim 1, wherein the
first sensor unit and the second sensor unit include transmission
means, the transmission means including at least one of an antenna
for wireless transmission and wires for wired transmission.
11. The tire wear state estimation system of claim 1, wherein the
tire construction data includes at least one of a tire type, tire
model, size information, manufacturing location, manufacturing
date, treadcap code, mold code, tire footprint shape factor, mold
design drop, tire belt/breaker angle, and overlay material.
12. The tire wear state estimation system of claim 1, wherein the
prediction model is a non-linear regression model.
13. The tire wear state estimation system of claim 12, wherein the
prediction model includes at least one of a Random Forest
Regressor, an XgBoost Regressor, and a CatBoost Regressor.
14. The tire wear state estimation system of claim 1, wherein the
processor includes transmission means to transmit the wear state
estimation to at least one of a display device, a service center, a
fleet manager and a vehicle control system.
15. A method for estimating the wear state of a tire supporting a
vehicle, the method comprising the steps of: mounting a first
sensor unit on the tire; measuring a footprint centerline length of
the tire with the first sensor unit; mounting a second sensor unit
on the tire; measuring a footprint shoulder length of the footprint
of the tire with the second sensor unit; measuring a pressure of
the tire with at least one of the first sensor unit and the second
sensor unit; measuring a temperature of the tire with at least one
of the first sensor unit and the second sensor unit; storing
identification information for the tire in at least one of the
first sensor unit and the second sensor unit; receiving the
measured centerline length, the measured pressure, the measured
temperature, the identification information and the measured
shoulder length in a processor; storing tire construction data in a
tire construction database that is in electronic communication with
the processor; correlating the identification information to the
tire construction data; storing an analysis module on the
processor; receiving the measured centerline length, the measured
pressure, the measured temperature, the identification information,
the tire construction data and the measured shoulder length as
inputs in the analysis module; and generating an estimated wear
state for the tire from the inputs with a prediction model in the
analysis module.
16. The method for estimating the wear state of a tire supporting a
vehicle of claim 15, further comprising the steps of: providing a
denormalization filter in the analysis module; receiving the
measured centerline length, the measure pressure, the measured
temperature, the identification information, and the tire
construction data as inputs in the denormalization filter; and
generating a normalized footprint length with the denormalization
filter.
17. The method for estimating the wear state of a tire supporting a
vehicle of claim 16, further comprising the steps of: storing a
historical log of footprint measurements on a historical footprint
measurement database being in electronic communication with the
processor; and correlating the normalized footprint length to the
historical log of footprint measurements, and taking an average of
the values.
18. The method for estimating the wear state of a tire supporting a
vehicle of claim 17, further comprising the steps of: providing a
time filter in the analysis module; and applying the average of the
values to the time filter.
19. The method for estimating the wear state of a tire supporting a
vehicle of claim 18, further comprising the steps of: providing a
footprint shape calculator; calculating a footprint shape factor
from the measured centerline length and the measured shoulder
length with the footprint shape calculator; receiving a regularized
footprint length for the tire from the footprint shape calculator;
and inputting the regularized footprint length into the prediction
model.
20. The method for estimating the wear state of a tire supporting a
vehicle of claim 15, further comprising the steps of: providing a
vehicle-mounted collection unit to collect measurements of a speed
of the vehicle and an inertia of the vehicle; receiving the speed
of the vehicle and the inertia of the vehicle as inputs into the
analysis module; providing an event filter in the analysis module;
and comparing the speed of the vehicle and the inertia of the
vehicle to threshold values with the event filter before performing
further analysis with the analysis module.
Description
FIELD OF THE INVENTION
[0001] The invention relates generally to tire monitoring systems.
More particularly, the invention relates to systems that predict
tire wear. Specifically, the invention is directed to a system and
method for estimating tire wear state based upon a change in the
length and the shape factor of the footprint of the tire.
BACKGROUND OF THE INVENTION
[0002] Tire wear plays an important role in vehicle factors such as
safety, reliability, and performance. Tread wear, which refers to
the loss of material from the tread of the tire, directly affects
such vehicle factors. As a result, it is desirable to monitor
and/or measure the amount of tread wear experienced by a tire,
which is indicated as the tire wear state. It is to be understood
that for the purpose of convenience, the terms "tread wear" and
"tire wear" may be used interchangeably.
[0003] One approach to the monitoring and/or measurement of tread
wear has been through the use of wear sensors disposed in the tire
tread, which has been referred to as a direct method or approach.
The direct approach to measuring tire wear from tire-mounted
sensors has multiple challenges. Placing the sensors in an uncured
or "green" tire to then be cured at high temperatures may cause
damage to the wear sensors. In addition, sensor durability can
prove to be an issue in meeting the millions of cycles requirement
for tires. Moreover, wear sensors in a direct measurement approach
must be small enough not to cause any uniformity problems as the
tire rotates at high speeds. Finally, wear sensors can be expensive
and add significantly to the cost of the tire.
[0004] Due to such challenges, alternative approaches have been
developed, which involve prediction of tread wear over the life of
the tire, including indirect estimations of the tire wear state.
These alternative approaches have experienced certain disadvantages
in the prior art due to a lack of optimum prediction techniques,
which reduces the accuracy and/or reliability of the tread wear
predictions. For example, many such techniques involve data or
information that is not easily obtained, such as non-standard
vehicle system signals, or data that is not accurate under all
driving conditions.
[0005] In addition, certain prior art techniques of indirectly
estimating tire wear involve obtaining data from the vehicle
controller area network, which is referred to in the art as the
vehicle CAN bus. It may be undesirably difficult to access or
utilize the vehicle CAN bus in an economical and reliable
manner.
[0006] As a result, there is a need in the art for a system and
method that accurately and reliably estimates tire wear state using
easily obtained and accurate parameters, and which can operate
independently of the vehicle CAN bus.
SUMMARY OF THE INVENTION
[0007] According to an aspect of an exemplary embodiment of the
invention, a tire wear state estimation system is provided. The
system includes a vehicle and a tire that supports the vehicle. A
first sensor unit is mounted on the tire and includes a footprint
centerline length measurement sensor to measure a centerline length
of a footprint of the tire. A second sensor unit is mounted on the
tire and includes a shoulder length measurement sensor to measure a
shoulder length of the footprint of the tire. At least one of the
first sensor unit and the second sensor unit includes a pressure
sensor to measure a pressure of the tire, a temperature sensor to
measure a temperature of the tire, and electronic memory capacity
for storing identification information for the tire. A processor is
in electronic communication with the first sensor unit and the
second sensor unit and receives the measured centerline length, the
measured pressure, the measured temperature, the identification
information, and the measured shoulder length. A tire construction
database stores tire construction data and is in electronic
communication with the processor. The identification information is
correlated to the tire construction data. An analysis module is
stored on the processor and receives the measured centerline
length, the measured pressure, the measured temperature, the
identification information, the tire construction data, and the
measured shoulder length as inputs. The analysis module includes a
prediction model that generates an estimated wear state for the
tire from the inputs.
[0008] According to another aspect of an exemplary embodiment of
the invention, a method for estimating the wear state of a tire
supporting a vehicle is provided. The method includes the steps of
mounting a first sensor unit on the tire and measuring a footprint
centerline length of the tire with the sensor unit. A second sensor
unit is mounted on the tire and a footprint shoulder length of the
footprint of the tire is measured with the second sensor unit. A
pressure of the tire and a temperature of the tire are measured
with at least one of the first sensor unit and the second sensor
unit, and identification information for the tire is stored in at
least one of the first sensor unit and the second sensor unit. The
measured centerline length, the measured pressure, the measured
temperature, the identification information, and the measured
shoulder length are received in a processor. Tire construction data
is stored in a tire construction database that is in electronic
communication with the processor, and the identification
information is correlated to the tire construction data. An
analysis module is stored on the processor, and the analysis module
receives the measured centerline length, the measured pressure, the
measured temperature, the identification information, the tire
construction data, and the measured shoulder length as inputs. An
estimated wear state for the tire is generated from the inputs with
a prediction model in the analysis module.
BRIEF DESCRIPTION OF THE DRAWINGS
[0009] The invention will be described by way of example and with
reference to the accompanying drawings, in which:
[0010] FIG. 1 is a schematic perspective view of a vehicle that
includes a tire employing an exemplary embodiment of the tire wear
state estimation system of the present invention;
[0011] FIG. 2 is a plan view of a footprint of the tire shown in
FIG. 1 in a new condition;
[0012] FIG. 3 is a plan view of a footprint of the tire shown in
FIG. 1 in a worn condition;
[0013] FIG. 4 is a schematic diagram of aspects of an exemplary
embodiment of the tire wear state estimation system of the present
invention;
[0014] FIG. 5 is a schematic diagram showing aspects of the
analysis module of the tire wear state estimation system shown in
FIG. 5;
[0015] FIG. 6 is a cross-sectional view of a portion of the tire
shown in FIG. 1;
[0016] FIG. 7 is a graphical representation of data observing
degrees of wear at different locations of tires;
[0017] FIG. 8 is a graphical representation of data showing the
relationship of the footprint shape factor of tires to the
remaining tread depth; and
[0018] FIG. 9 is a schematic diagram of the vehicle shown in FIG. 1
with a representation of data transmission to a cloud-based server
and to a user device.
[0019] Similar numerals refer to similar parts throughout the
drawings.
DEFINITIONS
[0020] "ANN" or "artificial neural network" is an adaptive tool for
non-linear statistical data modeling that changes its structure
based on external or internal information that flows through a
network during a learning phase. ANN neural networks are non-linear
statistical data modeling tools used to model complex relationships
between inputs and outputs or to find patterns in data.
[0021] "Axial" and "axially" means lines or directions that are
parallel to the axis of rotation of the tire.
[0022] "CAN bus" is an abbreviation for controller area
network.
[0023] "Circumferential" means lines or directions extending along
the perimeter of the surface of the annular tread perpendicular to
the axial direction.
[0024] "Equatorial centerplane (CP)" means the plane perpendicular
to the tire's axis of rotation and passing through the center of
the tread.
[0025] "Footprint" means the contact patch or area of contact
created by the tire tread with a flat surface as the tire rotates
or rolls.
[0026] "Inboard side" means the side of the tire nearest the
vehicle when the tire is mounted on a wheel and the wheel is
mounted on the vehicle.
[0027] "Lateral" means an axial direction.
[0028] "Outboard side" means the side of the tire farthest away
from the vehicle when the tire is mounted on a wheel and the wheel
is mounted on the vehicle.
[0029] "Radial" and "radially" means directions radially toward or
away from the axis of rotation of the tire.
[0030] "Rib" means a circumferentially extending strip of rubber on
the tread which is defined by at least one circumferential groove
and either a second such groove or a lateral edge, the strip being
laterally undivided by full-depth grooves.
[0031] "Tread element" or "traction element" means a rib or a block
element defined by a shape having adjacent grooves.
DETAILED DESCRIPTION OF THE INVENTION
[0032] With reference to FIGS. 1 through 9, an exemplary embodiment
of the tire wear state estimation system of the present invention
is indicated at 10. The tire wear state estimation system 10 and
accompanying method attempts to overcome the challenges posed by
prior art methods that measure the tire wear state through direct
sensor measurements. As such, the subject system and method is
referred herein as an "indirect" wear sensing system and method
that estimates wear state. The prior art direct approach to
measuring tire wear from tire-mounted sensors has multiple
challenges, which are described above. The tire wear estimation
state system 10 and accompanying method utilize an indirect
approach and avoid the problems attendant use of tire wear sensors
mounted directly to the tire tread.
[0033] With particular reference to FIG. 1, the system 10 estimates
the tread wear on each tire 12 supporting a vehicle 14. While the
vehicle 14 is depicted as a passenger car, the invention is not to
be so restricted. The principles of the invention find application
in other vehicle categories, such as commercial trucks, in which
vehicles may be supported by more or fewer tires than those shown
in FIG. 1.
[0034] The tires 12 are of conventional construction, and each tire
is mounted on a respective wheel 16 as known to those skilled in
the art. Each tire 12 includes a pair of sidewalls 18 (only one
shown) that extend to a circumferential tread 20, which wears with
age from road abrasion. An innerliner 22 is disposed on the inner
surface of the tire 12, and when the tire is mounted on the wheel
16, an internal cavity 24 is formed, which is filled with a
pressurized fluid, such as air.
[0035] A first sensor unit 26 is attached to the innerliner 22 of
each tire 12 by means such as an adhesive, and measures certain
parameters or conditions of the tire as will be described in
greater detail below. Preferably, the first sensor unit 26 is
attached to the innerliner 22 at an equatorial centerplane 92 of
the tire 12. Alternatively, the first sensor unit 26 may be
attached to other components of the tire 12, such as on or in one
of the sidewalls 18, on or in the tread 20, on the wheel 16, and/or
a combination thereof. For the purpose of convenience, reference
herein shall be made to mounting of the first sensor unit 26 on the
tire 12, with the understanding that such mounting includes all
such attachment.
[0036] The first sensor unit 26 is mounted on each tire 12 for the
purpose of detecting certain real-time tire parameters, such as
tire pressure 38 (FIG. 4) and temperature 40. For this reason, the
first sensor unit 26 preferably includes a pressure sensor and a
temperature sensor, and may be of any known configuration.
[0037] The first sensor unit 26 preferably also includes electronic
memory capacity for storing identification (ID) information for
each tire 12, known as tire ID information and indicated at 42
(FIG. 4). Alternatively, tire ID information 42 may be included in
another sensor unit, or in a separate tire ID storage medium, such
as a tire ID tag, which is in electronic communication with the
first sensor unit 26. The tire ID information 42 may include tire
parameter and/or manufacturing information, which will be described
in greater detail below.
[0038] Turning to FIG. 2, the first sensor unit 26 (FIG. 1)
preferably also measures a length 28 of a centerline 30 of a
footprint 32 of the tire 12. More particularly, as the tire 12
contacts the ground, the area of contact created by the tread 20
with the ground is known as the footprint 32. The centerline 30 of
the footprint 32 corresponds to the equatorial centerplane of the
tire 12, which is the plane that is perpendicular to the axis of
rotation of the tire and which passes through the center of the
tread 20. The first sensor unit 26 thus measures the length 28 of
the centerline 30 of the tire footprint 32, which is referred to
herein as the footprint centerline length 28. Any suitable
technique for measuring the footprint centerline length 28 may be
employed by the first sensor unit 26. For example, the first sensor
unit 26 may include a strain sensor or piezoelectric sensor that
measures deformation of the tread 20 and thus indicates the
centerline length 28.
[0039] It has been observed that, as the tire 12 wears, the
centerline length 28 decreases. For example, the footprint 32 shown
in FIG. 2 corresponds to a tire 12 in a new condition without tire
wear. FIG. 3 shows the footprint of the same tire 12 in a worn
state or condition after traveling about 21,000 kilometers (km).
After such travel, the tire 12 experienced about a 30 percent (%)
reduction of tread depth, as shown by the footprint after wear,
indicated at 32w, and a decrease of about 6% in the centerline
length, indicated by 28w, when compared to the new condition shown
in FIG. 2. This observation indicates that the centerline length
28, 28w may be an indicator of the wear state of the tire 12.
[0040] Further testing confirmed this observation, showing a
reduction of centerline length 28 corresponding to wear of the tire
12, including up to a 20% decrease in the centerline length when
the tread depth was reduced by 100%, or completely reduced to a
legal limit. It is to be understood that the first sensor unit 26
measures the centerline length 28, 28w of the tire 12 at a certain
point in time, and for the purpose of convenience, any such
measurement shall be referred to as the centerline length 28.
[0041] It is to be understood that the pressure sensor, the
temperature sensor, the tire ID capacity and/or the centerline
length sensor may be incorporated into the first sensor unit 26 as
a single unit, or may be incorporated into multiple units. For the
purpose of convenience, reference herein shall be made to the first
sensor unit 26 as a single unit.
[0042] With reference to FIG. 4, the first sensor unit 26 includes
transmission means 34 for sending the measured parameters of tire
pressure 38, tire temperature 40 and centerline length 28, as well
as tire ID information 42, to a processor 36. The transmission
means 34 may include an antenna for wireless transmission or wires
for wired transmission. The processor 36 may be integrated into the
first sensor unit 26, or may be a remote processor, which may be
mounted on the vehicle 14 or be cloud-based. For the purpose of
convenience, the processor 36 will be described as a remote
processor mounted on the vehicle 14, with the understanding that
the processor may alternatively be cloud-based or integrated into
the first sensor unit 26.
[0043] Aspects of the tire wear state estimation system 10
preferably are executed on the processor 36, which enables input of
data from the first sensor unit 26 and execution of specific
analysis techniques and algorithms, to be described below, which
are stored in a suitable storage medium and are also in electronic
communication with the processor.
[0044] In this manner, the first sensor unit 26 measures the tire
pressure 38, tire temperature 40 and centerline length 28, and
transmits these measured parameters to the processor 36 with the
tire ID information 42. The tire ID information 42 enables a tire
construction database 44 to be electronically accessed 46. The tire
construction database 44 stores tire construction data 50, which
will be described in greater detail below. The database 44 is in
electronic communication with the processor 36 and may be stored on
the processor, enabling transmission 48 of the tire construction
data 50 to the processor 36.
[0045] The tire ID information 42 may be correlated to specific
construction data 50 for each tire 12, including: the tire type;
tire model; size information, such as rim size, width, and outer
diameter; manufacturing location; manufacturing date; a treadcap
code that includes or correlates to a compound identification; a
mold code that includes or correlates to a tread structure
identification; a tire footprint shape factor (FSF), a mold design
drop; a tire belt/breaker angle; and an overlay material. The tire
ID information 42 may also correlate to a service history or other
information to identify specific features and parameters of each
tire 12, as well as mechanical characteristics of the tire, such as
cornering parameters, spring rate, load-inflation relationship, and
the like.
[0046] An analysis module 52 is stored on the processor 36, and
receives the tire pressure 38, tire temperature 40, tire centerline
length 28, tire ID information 42, and tire construction data 50.
The analysis module 52 analyzes these inputs to generate an
estimate of the tire wear state, indicated at 54, as will be
described in greater detail below.
[0047] Turning to FIG. 5, the analysis module 52 receives the
tire-based data inputs of tire pressure 38, tire temperature 40,
centerline length 28 and tire ID information 42. The analysis
module 52 preferably also receives data from a vehicle-mounted
collection unit 56. The data from the vehicle-mounted collection
unit 56 includes vehicle speed 58 as calculated from global
positioning system (GPS) data, and inertial measurements 60 for the
vehicle 14 from an accelerometer.
[0048] An event filter 62 is applied to the data received from the
vehicle-mounted collection unit 56. More particularly, vehicle
conditions are reviewed in the event filter 62, including the
measured vehicle speed 58 from GPS data and the inertial
measurements 60. These measured values are compared to threshold
values, including upper and lower limits. If the measured values
are outside of the threshold values, the system 10 does not
proceed, as the vehicle 14 is likely to be operating outside of
normal or predictable conditions. If the measured values are within
the threshold values, the measured data of tire pressure 38, tire
temperature 40, centerline length 28 and vehicle speed 58 are sent
to a denormalization filter 64.
[0049] The denormalization filter 64 is employed to account for and
eliminate the effect of inflation pressure 38, temperature 40 and
vehicle speed 58 on the centerline length 28 of the tire 12. In the
denormalization filter 64, a pre-trained regression model is used
to account for the effects of inflation pressure 38, temperature 40
and vehicle speed 58. Regardless of the vehicle and tire operating
conditions, the centerline length 28 is regressed to a pre-defined
nominal condition, that is, a pre-defined inflation pressure 38,
temperature 40 and vehicle speed 58. The denormalization filter 64
yields a normalized footprint length 66.
[0050] In addition, the fastest wearing portion of the tire 12 may
not always be at the centerline 30 (FIG. 2). For many tires, the
fastest wear may be at a shoulder 88. However, the difference
between the wear rate of the tire 12 at the centerline 30 and at
the shoulder 88 typically is dependent upon the tire construction
data 50, including the tire footprint shape factor (FSF), mold
design drop, tire belt/breaker angle and/or the overlay material.
The tire construction data 50 from the tire construction database
44 thus is input into the denormalization filter 64, and is used in
conjunction with the centerline length measurement 28 from the
first sensor unit 26 to estimate a length 90 at the shoulder 88,
which may be the fastest-wearing portion of the tread 20. The
technique for employing the tire footprint shape factor is
described in greater detail below.
[0051] The denormalization filter 64 generates a normalized
footprint length 66. Because the centerline length 28 of the tire
12 may also be affected by the vehicle load, the effect of load on
the normalized footprint length 66 must be accounted for and
eliminated. To eliminate the effect of load on the normalized
footprint length 66, a historical footprint measurement database 68
is accessed. The historical footprint measurement database 68 is in
electronic communication with the processor 36 and may be stored on
the processor, and contains a historical log of footprint
measurements 70. The normalized footprint length 66 is correlated
to the historical log 70 and an average of the values is taken.
[0052] The average of the values is applied to a time filter 72.
The time filter 72 accounts for time-scale decomposition of the
tire 12. More particularly, the time filter 72 accounts for and
eliminates bias due to factors or parameters that may affect the
tire 12 over time, and which are not among the above-described
measured parameters. The technique employed in the time filter 72
is described in greater detail in an Application titled "Method for
Extracting Changes in Tire Characteristics", which is being filed
concurrently with the instant Application by the same Assignee, The
Goodyear Tire & Rubber Company, and which is incorporated
herein in its entirety.
[0053] The resulting value from the time filter 72 is processed by
a footprint shape factor calculator 94. As mentioned above, the
fastest wearing portion of the tire 12 may be at a shoulder 88.
This point is supported by the graphs shown in FIG. 7, in which
observational data indicates that a larger percentage of tires 12
typically incur a greater degree of wear at a shoulder 88 (FIG. 2)
rather than at the centerline 30. In addition, referring to FIG. 8,
testing has shown a linear correlation between a footprint shape
factor (FSF) 98 of the tire 12 and a remaining tread depth 100 of
the tire, which is the tire wear state.
[0054] Thus, in order to improve the accuracy of the tire wear
state estimation 54, the wear state estimation system 10
specifically accounts for the footprint shape factor 98 in the
footprint shape factor calculator 94. The footprint shape factor 98
is the quotient of the footprint centerline length 28 divided by
the length 90 of the shoulder 88, represented as:
FSF = Footprint length measured at the tire centerline Footprint
length measured at the tire shoulder ( inside or outside )
##EQU00001##
As noted in the above equation, the length 90 of the shoulder 88,
also referred to as the shoulder length, may be at either an inside
shoulder or an outside shoulder of the footprint 32, or it may be
an average of the length at both the inside shoulder and the
outside shoulder.
[0055] Turning now to FIGS. 1 and 6, the first sensor unit 26 is
attached to the innerliner 22 at an equatorial centerplane 92 of
the tire 12 to measure the footprint centerline length 28, as
described above. A second sensor unit 96 is also mounted to the
tire 12. The second sensor unit 96 is spaced laterally apart from
the first sensor unit 26 and is also attached to the innerliner 22.
The second sensor unit 96 is disposed at or near a selected one of
the shoulders 88 (FIG. 2) to measure the shoulder length 90. Any
suitable technique for measuring the shoulder length 90 may be
employed by the second sensor unit 96. For example, the second
sensor unit 96 may include a strain sensor or piezoelectric sensor
that measures deformation of the tread 20 and thus indicates the
shoulder length 90. It is to be understood that either one or both
of the first sensor unit 26 and the second sensor unit 96 may
include the above-described pressor sensor, temperature sensor, and
tire ID capacity.
[0056] In this manner, the first sensor unit 26 measures the
footprint centerline length 28 and the second sensor unit 96
measures the footprint shoulder length 90. As with the first sensor
unit 26, the second sensor unit includes transmission means, which
may include an antenna for wireless transmission or wires for wired
transmission, for sending the measured shoulder length 90 to the
processor 36. Thus, the measurement of the centerline length 28 and
the measurement of the shoulder length 90 are both electronically
communicated from the respective first sensor unit 26 and the
second sensor unit 96 to the analysis module 52 in the processor
36. The footprint shape factor calculator 94 receives these
measurements, calculates the footprint shape factor 98, and yields
a regularized footprint length 74 for the tire 12.
[0057] The regularized footprint length 74 is input into a
prediction model 76 to generate the estimated wear state 54 for the
tire 12. The prediction model 76 preferably is a non-linear
regression model. By way of background, non-linear regression
models are a form of regression analysis in which observational
data are modeled by a function that is a nonlinear combination of
the model parameters, and depends on one or more independent
variables. Examples of non-linear regression models that may be
employed in the prediction model 76 include a Random Forest
Regressor, an XgBoost Regressor, and a CatBoost Regressor.
[0058] In this manner, the tire-based measured values of centerline
length 28, shoulder length 90, pressure 38 and temperature 40 are
input into the analysis module 52, along with the tire ID
information 42 and the vehicle-based measured values of speed 58
and inertia 60. The normalized footprint length 66 is generated
after the denormalization filter 64 is applied, and the regularized
footprint length 74 is generated after the normalized footprint
length is correlated to the historical log 70, an average of the
values is applied to the time filter 72, and that result is
processed by the footprint shape factor calculator 94. The
prediction model 76 employs the regularized footprint length 74 to
estimate the wear state 54 of the tire 12.
[0059] Referring to FIG. 9, when the wear state 54 is estimated for
each tire 12, the data may be wirelessly transmitted 78 from the
processor 36 on the vehicle 14 to a remote processor, such as a
processor in a cloud-based server 80. The wear state estimation 54
may be stored and/or remotely analyzed, and may also be wirelessly
transmitted 82 to a display device 84 for a display that is
accessible to a user of the vehicle 14, such as a smartphone.
Alternatively, the wear state estimation 54 may be wirelessly
transmitted 86 from the processor 36 directly to the display device
84.
[0060] In addition, the tire wear state estimation 54 may be
compared in the processor 36 to a predetermined wear limit. If the
wear state estimation 54 is below the limit of acceptable remaining
depth of the tread 20, a notice may be transmitted to the display
device 84. The tire wear state estimation system 10 thus may
provide notice or a recommendation to a vehicle operator that one
or more tires 12 are worn and should be replaced.
[0061] The tire wear state estimation system 10 may also transmit
or communicate the tire wear state estimation 54 to a service
center or a fleet manager. Moreover, the tire wear state estimation
system 10 may transmit or communicate the tire wear state
estimation 54 to an electronic control unit of the vehicle 14
and/or a vehicle control system, such as the braking system and/or
the suspension system, to increase the performance of such
systems.
[0062] In this manner, the tire wear state estimation system 10 of
the present invention estimates the wear state of the tire 12 by
measuring the tire-based parameters of footprint centerline length
28, footprint shoulder length 90, pressure 38 and temperature 40,
measuring the vehicle-based parameters of speed 58 and inertia 60,
and incorporating tire ID information 42. The system 10 inputs
these parameters and information into an analysis module 52, which
provides an accurate and reliable estimation of the tire wear state
54. The tire wear state estimation system 10 of the present
invention thus provides an independent, standalone system that does
not need to be integrated into the electronic systems of the
vehicle, including the CAN bus system.
[0063] The present invention also includes a method of estimating
the wear state of a tire 12. The method includes steps in
accordance with the description that is presented above and shown
in FIGS. 1 through 9.
[0064] It is to be understood that the structure and method of the
above-described tire wear state estimation system may be altered or
rearranged, or components or steps known to those skilled in the
art omitted or added, without affecting the overall concept or
operation of the invention. For example, electronic communication
may be through a wired connection or wireless communication without
affecting the overall concept or operation of the invention. Such
wireless communications include radio frequency (RF) and
Bluetooth.RTM. communications.
[0065] The invention has been described with reference to a
preferred embodiment. Potential modifications and alterations will
occur to others upon a reading and understanding of this
description. It is to be understood that all such modifications and
alterations are included in the scope of the invention as set forth
in the appended claims, or the equivalents thereof.
* * * * *